Daniel Giannella‐Neto

73 papers receiving 2.0k citations

Peers

Daniel Giannella‐Neto
Comparison fields: 5 of 107
  • Endocrinology, Diabetes and Metabolism 710
  • Endocrine and Autonomic Systems 87
  • Physiology 297
  • Epidemiology 337
  • Clinical Biochemistry 69
Replace Kun‐san Xiang with:
Kun‐san Xiang China
Mirjam V. Faulenbach United States
Nirmal Parajuli Canada
S Tarui Japan
Michael James Kraakman Australia
Silvia Pinach Italy
Akira Matsutani Japan
Hélène L. Kammoun Australia
Sanghoon Moon South Korea
Henrik Ullits Andersen Denmark
Daniel Giannella‐Neto relative to Kun‐san Xiang China Kun‐san Xiang's profile →
Citations per field
00.5×1.6×
Kun‐san Xiang · 1×
Citations per year

Countries citing papers authored by Daniel Giannella‐Neto

Since Specialization
Citations

This map shows the geographic impact of Daniel Giannella‐Neto's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Daniel Giannella‐Neto with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Daniel Giannella‐Neto more than expected).

Fields of papers citing papers by Daniel Giannella‐Neto

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Daniel Giannella‐Neto. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Daniel Giannella‐Neto. The network helps show where Daniel Giannella‐Neto may publish in the future.

Co-authors

The 25 scholars most cited alongside Daniel Giannella‐Neto, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Daniel Giannella‐Neto Line = papers co-authored together Daniel Giannella‐Neto links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 75 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2002312
2 1991106
3 199293
4 200191
5 200969
6 200666
7 201364
8 200863
9 199261
10
Effect of fat distribution on the pharmacokinetics of cortisol in obesity.
199860
11 200152
12 200048
13 200641
14 201839
15 201235
16 201735
17 200635
18 201433
19 200632
20 200631

About Daniel Giannella‐Neto

Daniel Giannella‐Neto is a scholar working on Endocrinology, Diabetes and Metabolism, Physiology, Surgery, Clinical Biochemistry and Oncology, having authored 75 papers that have together received 2.1k indexed citations. Recurring topics across this work include Growth Hormone and Insulin-like Growth Factors (9 papers), Pituitary Gland Disorders and Treatments (9 papers), Metabolism, Diabetes, and Cancer (6 papers), Pancreatic function and diabetes (6 papers), Bariatric Surgery and Outcomes (5 papers), Neuroblastoma Research and Treatments (4 papers), Pancreatic and Hepatic Oncology Research (4 papers) and Advanced Glycation End Products research (4 papers). The work is most often cited by research in Endocrinology, Diabetes and Metabolism (710 citations), Endocrine and Autonomic Systems (87 citations), Physiology (297 citations), Epidemiology (337 citations) and Clinical Biochemistry (69 citations). Daniel Giannella‐Neto has collaborated with scholars based in Brazil, United States and France. Frequent co-authors include BERNARDO LEO WAJCHENBERG, R. F. Santos, Maria Lúcia Corrêa‐Giannella, Marcello D. Bronstein, James S. Forrester, James A. Fagin, Bojan Cercek, Carlos Jose Pirola, Behrooz G. Sharifi and Vanessa Q. Passos. Their work appears in journals such as Diabetology & Metabolic Syndrome, Clinica Chimica Acta, Pancreas, Proceedings of the National Academy of Sciences and Pituitary.

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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